Secure and Efficient RNS Approach for Elliptic Curve Cryptography

Scalar multiplication, the main operation in elliptic curve cryptographic protocols, is vulnerable to side-channel (SCA) and fault injection (FA) attacks. An efficient countermeasure for scalar multiplication can be provided by using alternative number systems like the Residue Number System (RNS). In RNS, a number is represented as a set of smaller numbers, where each one is the result of the modular reduction with a given moduli basis. Under certain requirements, a number can be uniquely transformed from the integers to the RNS domain (and vice versa) and all arithmetic operations can be performed in RNS. This representation provides an inherent SCA and FA resistance to many attacks and can be further enhanced by RNS arithmetic manipulation or more traditional algorithmic countermeasures. In this paper, extending our previous work, we explore the potentials of RNS as an SCA and FA countermeasure and provide an description of RNS based SCA and FA resistance means. We propose a secure and efficient Montgomery Power Ladder based scalar multiplication algorithm on RNS and discuss its SCAFA resistance. The proposed algorithm is implemented on an ARM Cortex A7 processor and its SCA-FA resistance is evaluated by collecting preliminary leakage trace results that validate our initial assumptions

Machine-Learning assisted Side-Channel Attacks on RNS-based Elliptic Curve Implementations using Hybrid Feature Engineering

Side-channel attacks based on machine learning have recently been introduced to recover the secret information from software and hardware implementations of mathematically secure algorithms. Convolutional Neural Networks (CNNs) have proven to outperform the template attacks due to their ability of handling misalignment in the symmetric algorithms leakage data traces. However, one of the limitations of deep learning algorithms is the requirement of huge datasets for model training. For evaluation scenarios, where limited leakage trace instances are available, simple machine learning with the selection of proper feature engineering, data splitting, and validation techniques, can be more effective. Moreover, limited analysis exists for public-key algorithms, especially on non-traditional implementations like those using Residue Number System (RNS). Template attacks are successful on RNS-based Elliptic Curve Cryptography (ECC), only if the aligned portion is used in templates. In this study, we present a systematic methodology for the evaluation of ECC cryptosystems with and without countermeasures against machine learning side-channel attacks using two attack models. RNS-based ECC datasets have been evaluated using four machine learning classifiers and comparison is provided with existing state-of-the-art template attacks. Moreover, we analyze the impact of raw features and advanced hybrid feature engineering techniques, along with the effect of splitting ratio. We discuss the metrics and procedures that can be used for accurate classification on the imbalance datasets. The experimental results demonstrate that, for ECC RNS datasets, the efficiency of simple machine learning algorithms is better than the complex deep learning techniques when such datasets are not so huge

Energy Consumption Evaluation of Post-Quantum TLS 1.3 for Resource-Constrained Embedded Devices

Post-Quantum cryptography (PQC), in the past few years, constitutes the main driving force of the quantum resistance transition for security primitives, protocols and tools. TLS is one of the widely used security protocols that needs to be made quantum safe. However, PQC algorithms integration into TLS introduce various implementation overheads compared to traditional TLS that in battery powered embedded devices with constrained resources, cannot be overlooked. While there exist several works, evaluating the PQ TLS execution time overhead in embedded systems there are only a few that explore the PQ TLS energy consumption cost. In this paper, a thorough power/energy consumption evaluation and analysis of PQ TLS 1.3 on embedded systems has been made. A WolfSSL PQ TLS 1.3 custom implementation is used that integrates all the NIST PQC algorithms selected for standardisation as well as 2 out of 3 of those evaluated in NIST Round 4. Also 1 out of 2 of the BSI recommendations have been included. The PQ TLS 1.3 with the various PQC algorithms is deployed in a STM Nucleo evaluation board under a mutual and a unilateral client-server authentication scenario. The power and energy consumption collected results are analyzed in detail. The performed comparisons and overall analysis provide very interesting results indicating that the choice of the PQC algorithms in TLS 1.3 to be deployed on an embedded system may be very different depending on the device use as an authenticated or not authenticated, client or server. Also, the results indicate that in some cases, PQ TLS 1.3 implementations can be equally or more energy consumption efficient compared to traditional TLS 1.3

Performance Evaluation of Post-Quantum TLS 1.3 on Resource-Constrained Embedded Systems

Transport Layer Security (TLS) constitutes one of the most widely used protocols for securing Internet communications and has also found broad acceptance in the Internet of Things (IoT) domain. As we progress toward a security environment resistant to quantum computer attacks, TLS needs to be transformed to support post-quantum cryptography. However, post-quantum TLS is still not standardised, and its overall performance, especially in resource-constrained, IoT-capable, embedded devices, is not well understood. In this paper, we showcase how TLS 1.3 can be transformed into quantum-safe by modifying the TLS 1.3 architecture in order to accommodate the latest Post-Quantum Cryptography (PQC) algorithms from NIST PQC process. Furthermore, we evaluate the execution time, memory, and bandwidth requirements of this proposed post-quantum variant of TLS 1.3 (PQ TLS 1.3). This is facilitated by integrating the pqm4 and PQClean library implementations of almost all PQC algorithms selected for standardisation by the NIST PQC process, as well as the alternatives to be evaluated in a new round (Round 4). The proposed solution and evaluation focuses on the lower end of resource-constrained embedded devices. Thus, the evaluation is performed on the ARM Cortex-M4 embedded platform NUCLEO-F439ZI that provides $180$ MHz clock rate, $2$ MB Flash Memory, and $256$ KB SRAM. To the authors\u27 knowledge, this is the first systematic, thorough, and complete timing, memory usage, and network traffic evaluation of PQ TLS 1.3 for all the NIST PQC process selections and upcoming candidate algorithms, that explicitly targets resource-constrained embedded systems

Anomaly Detection Trusted Hardware Sensors for Critical Infrastructure Legacy Devices

Critical infrastructures and associated real time Informational systems need some security protection mechanisms that will be able to detect and respond to possible attacks. For this reason, Anomaly Detection Systems (ADS), as part of a Security Information and Event Management (SIEM) system, are needed for constantly monitoring and identifying potential threats inside an Information Technology (IT) system. Typically, ADS collect information from various sources within a CI system using security sensors or agents and correlate that information so as to identify anomaly events. Such sensors though in a CI setting (factories, power plants, remote locations) may be placed in open areas and left unattended, thus becoming targets themselves of security attacks. They can be tampering and malicious manipulated so that they provide false data that may lead an ADS or SIEM system to falsely comprehend the CI current security status. In this paper, we describe existing approaches on security monitoring in critical infrastructures and focus on how to collect security sensor–agent information in a secure and trusted way. We then introduce the concept of hardware assisted security sensor information collection that improves the level of trust (by hardware means) and also increases the responsiveness of the sensor. Thus, we propose a Hardware Security Token (HST) that when connected to a CI host, it acts as a secure anchor for security agent information collection. We describe the HST functionality, its association with a host device, its expected role and its log monitoring mechanism. We also provide information on how security can be established between the host device and the HST. Then, we introduce and describe the necessary host components that need to be established in order to guarantee a high security level and correct HST functionality. We also provide a realization–implementation of the HST overall concept in a FPGA SoC evaluation board and describe how the HST implementation can be controlled. In addition, in the paper, two case studies where the HST has been used in practice and its functionality have been validated (one case study on a real critical infrastructure test site and another where a critical industrial infrastructure was emulated in our lab) are described. Finally, results taken from these two case studies are presented, showing actual measurements for the in-field HST usage

End Node Security and Trust vulnerabilities in the Smart City Infrastructure

As cities gradually introduce intelligence in their core services and infrastructure thus becoming “smart cities”, they are deploying new Information Technology devices in the urban grid that are interconnected to a broad network. The main focus of widely implemented smart cities' services was the operation of sensors and smart devices across city areas that need low energy consumption and high connectivity. However, as 5G technologies are gradually been adopted in the smart city infrastructure thus solving that problem, the fundamental issue of addressing security becomes dominant. While latest network topologies and standards include security functions thus giving an illusion of security, there is little focus on the fact that many smart city end nodes cannot realize all security specifications without additional help. In this paper, we discuss briefly smart city security issues and focus on problem and security requirement that need to be address in the smart city end nodes, the sensors and actuators deployed within the city's grid. In this paper, attacks that cannot be thwarted by traditional cybersecurity solutions are discussed and countermeasures based on hardware are suggested in order to achieve a high level of trust. Also, the danger of microarchitectural and side channel attacks on these devices is highlighted and protection approaches are discussed

Machine Learning Attacks and Countermeasures on Hardware Binary Edwards Curve Scalar Multipliers

Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively be used as a backbone for introducing SCA countermeasures on Elliptic Curve Cryptography (ECC) scalar multipliers. The proposed design strategy is based on the decomposition of the round calculations of the Montgomery Power Ladder (MPL) algorithm and the Scalar Multiplication (SM) algorithm into the underlined finite field operations, and their restructuring into parallel-processed operation sets. Having as a basis the proposed design strategy, we showcase how advanced SCA countermeasures can be easily introduced, focusing on randomizing the projective coordinates of the MPL round’s ECC point results. To evaluate the design approach and its SCA countermeasures, several simple ML-based SCAs are performed, and an attack roadmap is provided. The proposed roadmap assumes attackers that do not have access to a huge number of leakage traces, and that have limited resources with which to mount Deep Learning attacks. The trained models’ performance reveals a high level of resistance against ML-based SCAs when including SCA countermeasures in the proposed design strategy

Improved throughput bit-serial multiplier for GF(2m) fields

High throughput is a crucial factor in bit-serial GF(2m) fields multiplication for a variety of different applications including cryptography, error coding detection and computer algebra. The throughput of a multiplier is dependent on the required number of clock cycles to reach a result and its critical path delay. However, most bit-serial GF(2m) multipliers do not manage to reduce the required number of clock cycles below the threshold of m clock cycles without increasing dramatically their critical path delay. This increase is more evident if a multiplier is designed to be versatile. In this article, a new versatile bit-serial MSB multiplier for GF(2m) fields is proposed that achieves a 50% increase on average in throughput when compared to other designs, with a very small increase in its critical path delay. This is achieved by an average 33.4% reduction in the required number of clock cycles below m. The proposed design can handle arbitrary bit-lengths upper bounded by m and is suitable for applications where the field order may vary

Practical Evaluation of Protected Residue Number System Scalar Multiplication

The Residue Number System (RNS) arithmetic is gaining grounds in public key cryptography, because it offers fast, efficient and secure implementations over large prime fields or rings of integers. In this paper, we propose a generic, thorough and analytic evaluation approach for protected scalar multiplication implementations with RNS and traditional Side Channel Attack (SCA) countermeasures in an effort to assess the SCA resistance of RNS. This paper constitutes the first robust evaluation of RNS software for Elliptic Curve Cryptography against electromagnetic (EM) side-channel attacks. Four different countermeasures, namely scalar and point randomization, random base permutations and random moduli operation sequence, are implemented and evaluated using the Test Vector Leakage Assessment (TVLA) and template attacks. More specifically, variations of RNS-based Montgomery Powering Ladder scalar multiplication algorithms are evaluated on an ARM Cortex A8 processor using an EM probe for acquisition of the traces. We show experimentally and theoretically that new bounds should be put forward when TVLA evaluations on public key algorithms are performed. On the security of RNS, our data and location dependent template attacks show that even protected implementations are vulnerable to these attacks. A combination of RNS-based countermeasures is the best way to protect against side-channel leakage